Overview

Dataset statistics

Number of variables32
Number of observations141947
Missing cells154010
Missing cells (%)3.4%
Duplicate rows10164
Duplicate rows (%)7.2%
Total size in memory34.7 MiB
Average record size in memory256.0 B

Variable types

Categorical18
Numeric14

Alerts

Dataset has 10164 (7.2%) duplicate rowsDuplicates
country has a high cardinality: 177 distinct valuesHigh cardinality
reservation_status_date has a high cardinality: 925 distinct valuesHigh cardinality
arrival_date_week_number is highly overall correlated with arrival_date_monthHigh correlation
agent is highly overall correlated with hotelHigh correlation
company is highly overall correlated with hotelHigh correlation
hotel is highly overall correlated with agent and 1 other fieldsHigh correlation
is_canceled is highly overall correlated with reservation_statusHigh correlation
arrival_date_month is highly overall correlated with arrival_date_week_numberHigh correlation
market_segment is highly overall correlated with distribution_channelHigh correlation
distribution_channel is highly overall correlated with market_segmentHigh correlation
reserved_room_type is highly overall correlated with assigned_room_typeHigh correlation
assigned_room_type is highly overall correlated with reserved_room_typeHigh correlation
reservation_status is highly overall correlated with is_canceledHigh correlation
children is highly imbalanced (81.6%)Imbalance
babies is highly imbalanced (97.1%)Imbalance
meal is highly imbalanced (53.5%)Imbalance
country is highly imbalanced (54.9%)Imbalance
distribution_channel is highly imbalanced (62.9%)Imbalance
is_repeated_guest is highly imbalanced (79.9%)Imbalance
reserved_room_type is highly imbalanced (59.4%)Imbalance
assigned_room_type is highly imbalanced (51.7%)Imbalance
deposit_type is highly imbalanced (64.1%)Imbalance
required_car_parking_spaces is highly imbalanced (85.5%)Imbalance
agent has 19555 (13.8%) missing valuesMissing
company has 133822 (94.3%) missing valuesMissing
adults is highly skewed (γ1 = 24.26609101)Skewed
previous_cancellations is highly skewed (γ1 = 20.2286875)Skewed
previous_bookings_not_canceled is highly skewed (γ1 = 25.29075503)Skewed
lead_time has 7861 (5.5%) zerosZeros
stays_in_weekend_nights has 62065 (43.7%) zerosZeros
stays_in_week_nights has 8881 (6.3%) zerosZeros
previous_cancellations has 131027 (92.3%) zerosZeros
previous_bookings_not_canceled has 138008 (97.2%) zerosZeros
booking_changes has 120861 (85.1%) zerosZeros
days_in_waiting_list has 137500 (96.9%) zerosZeros
adr has 2555 (1.8%) zerosZeros
total_of_special_requests has 86024 (60.6%) zerosZeros

Reproduction

Analysis started2023-04-12 02:48:14.911418
Analysis finished2023-04-12 02:50:50.918432
Duration2 minutes and 36.01 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

hotel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
City Hotel
93103 
Resort Hotel
48844 

Length

Max length12
Median length10
Mean length10.688201
Min length10

Characters and Unicode

Total characters1517158
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResort Hotel
2nd rowResort Hotel
3rd rowResort Hotel
4th rowResort Hotel
5th rowResort Hotel

Common Values

ValueCountFrequency (%)
City Hotel 93103
65.6%
Resort Hotel 48844
34.4%

Length

2023-04-11T23:50:51.188546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T23:50:51.639391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
hotel 141947
50.0%
city 93103
32.8%
resort 48844
 
17.2%

Most occurring characters

ValueCountFrequency (%)
t 283894
18.7%
o 190791
12.6%
e 190791
12.6%
141947
9.4%
H 141947
9.4%
l 141947
9.4%
C 93103
 
6.1%
i 93103
 
6.1%
y 93103
 
6.1%
R 48844
 
3.2%
Other values (2) 97688
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1091317
71.9%
Uppercase Letter 283894
 
18.7%
Space Separator 141947
 
9.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 283894
26.0%
o 190791
17.5%
e 190791
17.5%
l 141947
13.0%
i 93103
 
8.5%
y 93103
 
8.5%
s 48844
 
4.5%
r 48844
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
H 141947
50.0%
C 93103
32.8%
R 48844
 
17.2%
Space Separator
ValueCountFrequency (%)
141947
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1375211
90.6%
Common 141947
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 283894
20.6%
o 190791
13.9%
e 190791
13.9%
H 141947
10.3%
l 141947
10.3%
C 93103
 
6.8%
i 93103
 
6.8%
y 93103
 
6.8%
R 48844
 
3.6%
s 48844
 
3.6%
Common
ValueCountFrequency (%)
141947
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1517158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 283894
18.7%
o 190791
12.6%
e 190791
12.6%
141947
9.4%
H 141947
9.4%
l 141947
9.4%
C 93103
 
6.1%
i 93103
 
6.1%
y 93103
 
6.1%
R 48844
 
3.2%
Other values (2) 97688
 
6.4%

is_canceled
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
89108 
1
52839 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters141947
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 89108
62.8%
1 52839
37.2%

Length

2023-04-11T23:50:51.989303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T23:50:52.381805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 89108
62.8%
1 52839
37.2%

Most occurring characters

ValueCountFrequency (%)
0 89108
62.8%
1 52839
37.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 141947
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89108
62.8%
1 52839
37.2%

Most occurring scripts

ValueCountFrequency (%)
Common 141947
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 89108
62.8%
1 52839
37.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 89108
62.8%
1 52839
37.2%

lead_time
Real number (ℝ)

Distinct479
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.8211
Minimum0
Maximum737
Zeros7861
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-11T23:50:52.763066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median68
Q3158
95-th percentile320
Maximum737
Range737
Interquartile range (IQR)141

Descriptive statistics

Standard deviation106.47857
Coefficient of variation (CV)1.0355712
Kurtosis1.483849
Mean102.8211
Median Absolute Deviation (MAD)59
Skewness1.3180998
Sum14595147
Variance11337.686
MonotonicityNot monotonic
2023-04-11T23:50:53.237732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7861
 
5.5%
1 4153
 
2.9%
2 2453
 
1.7%
3 2193
 
1.5%
4 2033
 
1.4%
5 1926
 
1.4%
6 1756
 
1.2%
7 1553
 
1.1%
8 1376
 
1.0%
12 1358
 
1.0%
Other values (469) 115285
81.2%
ValueCountFrequency (%)
0 7861
5.5%
1 4153
2.9%
2 2453
 
1.7%
3 2193
 
1.5%
4 2033
 
1.4%
5 1926
 
1.4%
6 1756
 
1.2%
7 1553
 
1.1%
8 1376
 
1.0%
9 1146
 
0.8%
ValueCountFrequency (%)
737 2
 
< 0.1%
709 1
 
< 0.1%
629 17
< 0.1%
626 30
< 0.1%
622 17
< 0.1%
615 17
< 0.1%
608 17
< 0.1%
605 30
< 0.1%
601 17
< 0.1%
594 17
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2019
79264 
2020
40687 
2018
21996 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters567788
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2019 79264
55.8%
2020 40687
28.7%
2018 21996
 
15.5%

Length

2023-04-11T23:50:53.714566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T23:50:54.142516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2019 79264
55.8%
2020 40687
28.7%
2018 21996
 
15.5%

Most occurring characters

ValueCountFrequency (%)
2 182634
32.2%
0 182634
32.2%
1 101260
17.8%
9 79264
14.0%
8 21996
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 567788
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 182634
32.2%
0 182634
32.2%
1 101260
17.8%
9 79264
14.0%
8 21996
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common 567788
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 182634
32.2%
0 182634
32.2%
1 101260
17.8%
9 79264
14.0%
8 21996
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 567788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 182634
32.2%
0 182634
32.2%
1 101260
17.8%
9 79264
14.0%
8 21996
 
3.9%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
August
17772 
October
16127 
September
15630 
July
15446 
May
11791 
Other values (7)
65181 

Length

Max length9
Median length7
Mean length6.1032146
Min length3

Characters and Unicode

Total characters866333
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJuly
2nd rowJuly
3rd rowJuly
4th rowJuly
5th rowJuly

Common Values

ValueCountFrequency (%)
August 17772
12.5%
October 16127
11.4%
September 15630
11.0%
July 15446
10.9%
May 11791
8.3%
April 11089
7.8%
June 10939
7.7%
December 9812
6.9%
March 9810
6.9%
November 9149
6.4%
Other values (2) 14382
10.1%

Length

2023-04-11T23:50:55.208409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august 17772
12.5%
october 16127
11.4%
september 15630
11.0%
july 15446
10.9%
may 11791
8.3%
april 11089
7.8%
june 10939
7.7%
december 9812
6.9%
march 9810
6.9%
november 9149
6.4%
Other values (2) 14382
10.1%

Most occurring characters

ValueCountFrequency (%)
e 130062
15.0%
r 94371
 
10.9%
u 76311
 
8.8%
b 59090
 
6.8%
t 49529
 
5.7%
a 41993
 
4.8%
y 41619
 
4.8%
c 35749
 
4.1%
m 34591
 
4.0%
J 32395
 
3.7%
Other values (16) 270623
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 724386
83.6%
Uppercase Letter 141947
 
16.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 130062
18.0%
r 94371
13.0%
u 76311
10.5%
b 59090
8.2%
t 49529
 
6.8%
a 41993
 
5.8%
y 41619
 
5.7%
c 35749
 
4.9%
m 34591
 
4.8%
p 26719
 
3.7%
Other values (8) 134352
18.5%
Uppercase Letter
ValueCountFrequency (%)
J 32395
22.8%
A 28861
20.3%
M 21601
15.2%
O 16127
11.4%
S 15630
11.0%
D 9812
 
6.9%
N 9149
 
6.4%
F 8372
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 866333
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 130062
15.0%
r 94371
 
10.9%
u 76311
 
8.8%
b 59090
 
6.8%
t 49529
 
5.7%
a 41993
 
4.8%
y 41619
 
4.8%
c 35749
 
4.1%
m 34591
 
4.0%
J 32395
 
3.7%
Other values (16) 270623
31.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 866333
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 130062
15.0%
r 94371
 
10.9%
u 76311
 
8.8%
b 59090
 
6.8%
t 49529
 
5.7%
a 41993
 
4.8%
y 41619
 
4.8%
c 35749
 
4.1%
m 34591
 
4.0%
J 32395
 
3.7%
Other values (16) 270623
31.2%

arrival_date_week_number
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.061058
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-11T23:50:55.655246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q118
median30
Q340
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.604758
Coefficient of variation (CV)0.46814392
Kurtosis-0.94553904
Mean29.061058
Median Absolute Deviation (MAD)11
Skewness-0.20504069
Sum4125130
Variance185.08943
MonotonicityNot monotonic
2023-04-11T23:50:56.137806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 4750
 
3.3%
41 4102
 
2.9%
38 4033
 
2.8%
42 3979
 
2.8%
32 3898
 
2.7%
39 3886
 
2.7%
30 3857
 
2.7%
34 3811
 
2.7%
29 3461
 
2.4%
40 3427
 
2.4%
Other values (43) 102743
72.4%
ValueCountFrequency (%)
1 1059
0.7%
2 1251
0.9%
3 1330
0.9%
4 1498
1.1%
5 1400
1.0%
6 1562
1.1%
7 2250
1.6%
8 2244
1.6%
9 2190
1.5%
10 2174
1.5%
ValueCountFrequency (%)
53 2636
1.9%
52 1708
1.2%
51 1249
 
0.9%
50 2117
1.5%
49 2629
1.9%
48 2121
1.5%
47 2371
1.7%
46 1999
1.4%
45 2492
1.8%
44 3139
2.2%

arrival_date_day_of_month
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.74897
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-11T23:50:56.594131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7373137
Coefficient of variation (CV)0.55478637
Kurtosis-1.1937217
Mean15.74897
Median Absolute Deviation (MAD)8
Skewness-0.0050601603
Sum2235519
Variance76.340651
MonotonicityNot monotonic
2023-04-11T23:50:56.987909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
28 8663
 
6.1%
5 5506
 
3.9%
17 5343
 
3.8%
25 5049
 
3.6%
18 4931
 
3.5%
15 4931
 
3.5%
9 4926
 
3.5%
16 4924
 
3.5%
12 4894
 
3.4%
26 4825
 
3.4%
Other values (20) 87955
62.0%
ValueCountFrequency (%)
1 4216
3.0%
2 4686
3.3%
3 4622
3.3%
4 4487
3.2%
5 5506
3.9%
6 4512
3.2%
7 4319
3.0%
8 4727
3.3%
9 4926
3.5%
10 4358
3.1%
ValueCountFrequency (%)
31 2738
 
1.9%
30 4690
3.3%
28 8663
6.1%
27 4473
3.2%
26 4825
3.4%
25 5049
3.6%
24 4751
3.3%
23 4360
3.1%
22 4170
2.9%
21 4410
3.1%

stays_in_weekend_nights
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.92755747
Minimum0
Maximum19
Zeros62065
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-11T23:50:57.381958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0008171
Coefficient of variation (CV)1.0789813
Kurtosis7.2352028
Mean0.92755747
Median Absolute Deviation (MAD)1
Skewness1.3822691
Sum131664
Variance1.0016349
MonotonicityNot monotonic
2023-04-11T23:50:57.733700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 62065
43.7%
2 39780
28.0%
1 35967
25.3%
4 2212
 
1.6%
3 1514
 
1.1%
6 167
 
0.1%
5 100
 
0.1%
8 71
 
0.1%
7 31
 
< 0.1%
9 15
 
< 0.1%
Other values (7) 25
 
< 0.1%
ValueCountFrequency (%)
0 62065
43.7%
1 35967
25.3%
2 39780
28.0%
3 1514
 
1.1%
4 2212
 
1.6%
5 100
 
0.1%
6 167
 
0.1%
7 31
 
< 0.1%
8 71
 
0.1%
9 15
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 2
 
< 0.1%
16 3
 
< 0.1%
14 2
 
< 0.1%
13 4
 
< 0.1%
12 6
 
< 0.1%
10 7
 
< 0.1%
9 15
 
< 0.1%
8 71
0.1%
7 31
< 0.1%

stays_in_week_nights
Real number (ℝ)

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4955089
Minimum0
Maximum50
Zeros8881
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-11T23:50:58.143795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum50
Range50
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9097836
Coefficient of variation (CV)0.76528825
Kurtosis24.026218
Mean2.4955089
Median Absolute Deviation (MAD)1
Skewness2.8594249
Sum354230
Variance3.6472735
MonotonicityNot monotonic
2023-04-11T23:50:58.580646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2 40810
28.8%
1 36419
25.7%
3 25612
18.0%
5 13113
 
9.2%
4 11209
 
7.9%
0 8881
 
6.3%
6 1857
 
1.3%
10 1252
 
0.9%
7 1248
 
0.9%
8 789
 
0.6%
Other values (25) 757
 
0.5%
ValueCountFrequency (%)
0 8881
 
6.3%
1 36419
25.7%
2 40810
28.8%
3 25612
18.0%
4 11209
 
7.9%
5 13113
 
9.2%
6 1857
 
1.3%
7 1248
 
0.9%
8 789
 
0.6%
9 279
 
0.2%
ValueCountFrequency (%)
50 1
 
< 0.1%
42 2
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
33 2
 
< 0.1%
32 1
 
< 0.1%
30 6
< 0.1%
26 1
 
< 0.1%

adults
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8525506
Minimum0
Maximum55
Zeros448
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-11T23:50:58.933280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6285531
Coefficient of variation (CV)0.33929065
Kurtosis1640.728
Mean1.8525506
Median Absolute Deviation (MAD)0
Skewness24.266091
Sum262964
Variance0.395079
MonotonicityNot monotonic
2023-04-11T23:50:59.253306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 107169
75.5%
1 27564
 
19.4%
3 6664
 
4.7%
0 448
 
0.3%
4 70
 
< 0.1%
26 10
 
< 0.1%
27 4
 
< 0.1%
20 4
 
< 0.1%
5 4
 
< 0.1%
40 2
 
< 0.1%
Other values (4) 8
 
< 0.1%
ValueCountFrequency (%)
0 448
 
0.3%
1 27564
 
19.4%
2 107169
75.5%
3 6664
 
4.7%
4 70
 
< 0.1%
5 4
 
< 0.1%
6 2
 
< 0.1%
10 2
 
< 0.1%
20 4
 
< 0.1%
26 10
 
< 0.1%
ValueCountFrequency (%)
55 2
 
< 0.1%
50 2
 
< 0.1%
40 2
 
< 0.1%
27 4
 
< 0.1%
26 10
 
< 0.1%
20 4
 
< 0.1%
10 2
 
< 0.1%
6 2
 
< 0.1%
5 4
 
< 0.1%
4 70
< 0.1%

children
Categorical

Distinct5
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Memory size1.1 MiB
0.0
132400 
1.0
 
5358
2.0
 
4097
3.0
 
81
10.0
 
3

Length

Max length4
Median length3
Mean length3.0000211
Min length3

Characters and Unicode

Total characters425820
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 132400
93.3%
1.0 5358
 
3.8%
2.0 4097
 
2.9%
3.0 81
 
0.1%
10.0 3
 
< 0.1%
(Missing) 8
 
< 0.1%

Length

2023-04-11T23:50:59.672877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T23:51:00.116891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 132400
93.3%
1.0 5358
 
3.8%
2.0 4097
 
2.9%
3.0 81
 
0.1%
10.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 274342
64.4%
. 141939
33.3%
1 5361
 
1.3%
2 4097
 
1.0%
3 81
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 283881
66.7%
Other Punctuation 141939
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 274342
96.6%
1 5361
 
1.9%
2 4097
 
1.4%
3 81
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 141939
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 425820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 274342
64.4%
. 141939
33.3%
1 5361
 
1.3%
2 4097
 
1.0%
3 81
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 425820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 274342
64.4%
. 141939
33.3%
1 5361
 
1.3%
2 4097
 
1.0%
3 81
 
< 0.1%

babies
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
140813 
1
 
1114
2
 
17
9
 
2
10
 
1

Length

Max length2
Median length1
Mean length1.000007
Min length1

Characters and Unicode

Total characters141948
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 140813
99.2%
1 1114
 
0.8%
2 17
 
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%

Length

2023-04-11T23:51:00.502897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T23:51:00.948788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 140813
99.2%
1 1114
 
0.8%
2 17
 
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 140814
99.2%
1 1115
 
0.8%
2 17
 
< 0.1%
9 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 141948
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 140814
99.2%
1 1115
 
0.8%
2 17
 
< 0.1%
9 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 141948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 140814
99.2%
1 1115
 
0.8%
2 17
 
< 0.1%
9 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 140814
99.2%
1 1115
 
0.8%
2 17
 
< 0.1%
9 2
 
< 0.1%

meal
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
BB
109878 
HB
18402 
SC
11067 
Undefined
 
1372
FB
 
1228

Length

Max length9
Median length2
Mean length2.0676591
Min length2

Characters and Unicode

Total characters293498
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowHB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB 109878
77.4%
HB 18402
 
13.0%
SC 11067
 
7.8%
Undefined 1372
 
1.0%
FB 1228
 
0.9%

Length

2023-04-11T23:51:01.361237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T23:51:01.825260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
bb 109878
77.4%
hb 18402
 
13.0%
sc 11067
 
7.8%
undefined 1372
 
1.0%
fb 1228
 
0.9%

Most occurring characters

ValueCountFrequency (%)
B 239386
81.6%
H 18402
 
6.3%
S 11067
 
3.8%
C 11067
 
3.8%
n 2744
 
0.9%
d 2744
 
0.9%
e 2744
 
0.9%
U 1372
 
0.5%
f 1372
 
0.5%
i 1372
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 282522
96.3%
Lowercase Letter 10976
 
3.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 239386
84.7%
H 18402
 
6.5%
S 11067
 
3.9%
C 11067
 
3.9%
U 1372
 
0.5%
F 1228
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
n 2744
25.0%
d 2744
25.0%
e 2744
25.0%
f 1372
12.5%
i 1372
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 293498
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 239386
81.6%
H 18402
 
6.3%
S 11067
 
3.8%
C 11067
 
3.8%
n 2744
 
0.9%
d 2744
 
0.9%
e 2744
 
0.9%
U 1372
 
0.5%
f 1372
 
0.5%
i 1372
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 293498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 239386
81.6%
H 18402
 
6.3%
S 11067
 
3.8%
C 11067
 
3.8%
n 2744
 
0.9%
d 2744
 
0.9%
e 2744
 
0.9%
U 1372
 
0.5%
f 1372
 
0.5%
i 1372
 
0.5%

country
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct177
Distinct (%)0.1%
Missing625
Missing (%)0.4%
Memory size1.1 MiB
PRT
62384 
GBR
13487 
FRA
11792 
ESP
10519 
DEU
7813 
Other values (172)
35327 

Length

Max length3
Median length3
Mean length2.9900086
Min length2

Characters and Unicode

Total characters422554
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowPRT
4th rowPRT
5th rowPRT

Common Values

ValueCountFrequency (%)
PRT 62384
43.9%
GBR 13487
 
9.5%
FRA 11792
 
8.3%
ESP 10519
 
7.4%
DEU 7813
 
5.5%
ITA 4321
 
3.0%
IRL 3860
 
2.7%
BEL 2554
 
1.8%
BRA 2374
 
1.7%
NLD 2304
 
1.6%
Other values (167) 19914
 
14.0%

Length

2023-04-11T23:51:02.223238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
prt 62384
44.1%
gbr 13487
 
9.5%
fra 11792
 
8.3%
esp 10519
 
7.4%
deu 7813
 
5.5%
ita 4321
 
3.1%
irl 3860
 
2.7%
bel 2554
 
1.8%
bra 2374
 
1.7%
nld 2304
 
1.6%
Other values (167) 19914
 
14.1%

Most occurring characters

ValueCountFrequency (%)
R 98468
23.3%
P 74386
17.6%
T 68782
16.3%
E 24565
 
5.8%
A 24172
 
5.7%
B 18803
 
4.4%
S 16484
 
3.9%
G 14598
 
3.5%
U 14401
 
3.4%
F 12379
 
2.9%
Other values (16) 55516
13.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 422554
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 98468
23.3%
P 74386
17.6%
T 68782
16.3%
E 24565
 
5.8%
A 24172
 
5.7%
B 18803
 
4.4%
S 16484
 
3.9%
G 14598
 
3.5%
U 14401
 
3.4%
F 12379
 
2.9%
Other values (16) 55516
13.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 422554
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 98468
23.3%
P 74386
17.6%
T 68782
16.3%
E 24565
 
5.8%
A 24172
 
5.7%
B 18803
 
4.4%
S 16484
 
3.9%
G 14598
 
3.5%
U 14401
 
3.4%
F 12379
 
2.9%
Other values (16) 55516
13.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 422554
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 98468
23.3%
P 74386
17.6%
T 68782
16.3%
E 24565
 
5.8%
A 24172
 
5.7%
B 18803
 
4.4%
S 16484
 
3.9%
G 14598
 
3.5%
U 14401
 
3.4%
F 12379
 
2.9%
Other values (16) 55516
13.1%

market_segment
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Online TA
62840 
Offline TA/TO
30379 
Groups
26115 
Direct
14973 
Corporate
6486 
Other values (3)
 
1154

Length

Max length13
Median length9
Mean length9.0117438
Min length6

Characters and Unicode

Total characters1279190
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOnline TA
2nd rowOffline TA/TO
3rd rowOnline TA
4th rowOnline TA
5th rowDirect

Common Values

ValueCountFrequency (%)
Online TA 62840
44.3%
Offline TA/TO 30379
21.4%
Groups 26115
18.4%
Direct 14973
 
10.5%
Corporate 6486
 
4.6%
Complementary 913
 
0.6%
Aviation 237
 
0.2%
Undefined 4
 
< 0.1%

Length

2023-04-11T23:51:02.603468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T23:51:03.160889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
online 62840
26.7%
ta 62840
26.7%
offline 30379
12.9%
ta/to 30379
12.9%
groups 26115
11.1%
direct 14973
 
6.4%
corporate 6486
 
2.8%
complementary 913
 
0.4%
aviation 237
 
0.1%
undefined 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 157217
12.3%
O 123598
9.7%
T 123598
9.7%
e 116512
9.1%
i 108670
8.5%
l 94132
 
7.4%
A 93456
 
7.3%
93219
 
7.3%
f 60762
 
4.8%
r 54973
 
4.3%
Other values (16) 253053
19.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 766449
59.9%
Uppercase Letter 389143
30.4%
Space Separator 93219
 
7.3%
Other Punctuation 30379
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 157217
20.5%
e 116512
15.2%
i 108670
14.2%
l 94132
12.3%
f 60762
 
7.9%
r 54973
 
7.2%
o 40237
 
5.2%
p 33514
 
4.4%
s 26115
 
3.4%
u 26115
 
3.4%
Other values (7) 48202
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
O 123598
31.8%
T 123598
31.8%
A 93456
24.0%
G 26115
 
6.7%
D 14973
 
3.8%
C 7399
 
1.9%
U 4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
93219
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 30379
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1155592
90.3%
Common 123598
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 157217
13.6%
O 123598
10.7%
T 123598
10.7%
e 116512
10.1%
i 108670
9.4%
l 94132
8.1%
A 93456
8.1%
f 60762
 
5.3%
r 54973
 
4.8%
o 40237
 
3.5%
Other values (14) 182437
15.8%
Common
ValueCountFrequency (%)
93219
75.4%
/ 30379
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1279190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 157217
12.3%
O 123598
9.7%
T 123598
9.7%
e 116512
9.1%
i 108670
8.5%
l 94132
 
7.4%
A 93456
 
7.3%
93219
 
7.3%
f 60762
 
4.8%
r 54973
 
4.3%
Other values (16) 253053
19.8%

distribution_channel
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
TA/TO
116042 
Direct
17534 
Corporate
 
8167
GDS
 
194
Undefined
 
10

Length

Max length9
Median length5
Mean length5.3512156
Min length3

Characters and Unicode

Total characters759589
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA/TO
2nd rowTA/TO
3rd rowTA/TO
4th rowTA/TO
5th rowDirect

Common Values

ValueCountFrequency (%)
TA/TO 116042
81.8%
Direct 17534
 
12.4%
Corporate 8167
 
5.8%
GDS 194
 
0.1%
Undefined 10
 
< 0.1%

Length

2023-04-11T23:51:03.641104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T23:51:04.116955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
ta/to 116042
81.8%
direct 17534
 
12.4%
corporate 8167
 
5.8%
gds 194
 
0.1%
undefined 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 232084
30.6%
/ 116042
15.3%
O 116042
15.3%
A 116042
15.3%
r 33868
 
4.5%
e 25721
 
3.4%
t 25701
 
3.4%
D 17728
 
2.3%
i 17544
 
2.3%
c 17534
 
2.3%
Other values (10) 41283
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 490461
64.6%
Lowercase Letter 153086
 
20.2%
Other Punctuation 116042
 
15.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 33868
22.1%
e 25721
16.8%
t 25701
16.8%
i 17544
11.5%
c 17534
11.5%
o 16334
10.7%
a 8167
 
5.3%
p 8167
 
5.3%
n 20
 
< 0.1%
d 20
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
T 232084
47.3%
O 116042
23.7%
A 116042
23.7%
D 17728
 
3.6%
C 8167
 
1.7%
G 194
 
< 0.1%
S 194
 
< 0.1%
U 10
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 116042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 643547
84.7%
Common 116042
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 232084
36.1%
O 116042
18.0%
A 116042
18.0%
r 33868
 
5.3%
e 25721
 
4.0%
t 25701
 
4.0%
D 17728
 
2.8%
i 17544
 
2.7%
c 17534
 
2.7%
o 16334
 
2.5%
Other values (9) 24949
 
3.9%
Common
ValueCountFrequency (%)
/ 116042
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 759589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 232084
30.6%
/ 116042
15.3%
O 116042
15.3%
A 116042
15.3%
r 33868
 
4.5%
e 25721
 
3.4%
t 25701
 
3.4%
D 17728
 
2.3%
i 17544
 
2.3%
c 17534
 
2.3%
Other values (10) 41283
 
5.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
137495 
1
 
4452

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters141947
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 137495
96.9%
1 4452
 
3.1%

Length

2023-04-11T23:51:04.497754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T23:51:04.886736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 137495
96.9%
1 4452
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 137495
96.9%
1 4452
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 141947
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 137495
96.9%
1 4452
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 141947
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 137495
96.9%
1 4452
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 137495
96.9%
1 4452
 
3.1%

previous_cancellations
Real number (ℝ)

SKEWED  ZEROS 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12541301
Minimum0
Maximum26
Zeros131027
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-11T23:51:05.182816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0596306
Coefficient of variation (CV)8.4491288
Kurtosis448.44148
Mean0.12541301
Median Absolute Deviation (MAD)0
Skewness20.228688
Sum17802
Variance1.1228171
MonotonicityNot monotonic
2023-04-11T23:51:05.566465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 131027
92.3%
1 10313
 
7.3%
2 147
 
0.1%
24 96
 
0.1%
3 73
 
0.1%
26 52
 
< 0.1%
25 50
 
< 0.1%
19 38
 
< 0.1%
11 37
 
< 0.1%
4 31
 
< 0.1%
Other values (5) 83
 
0.1%
ValueCountFrequency (%)
0 131027
92.3%
1 10313
 
7.3%
2 147
 
0.1%
3 73
 
0.1%
4 31
 
< 0.1%
5 19
 
< 0.1%
6 22
 
< 0.1%
11 37
 
< 0.1%
13 12
 
< 0.1%
14 28
 
< 0.1%
ValueCountFrequency (%)
26 52
< 0.1%
25 50
< 0.1%
24 96
0.1%
21 2
 
< 0.1%
19 38
 
< 0.1%
14 28
 
< 0.1%
13 12
 
< 0.1%
11 37
 
< 0.1%
6 22
 
< 0.1%
5 19
 
< 0.1%

previous_bookings_not_canceled
Real number (ℝ)

SKEWED  ZEROS 

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1203266
Minimum0
Maximum72
Zeros138008
Zeros (%)97.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-11T23:51:06.026888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.381798
Coefficient of variation (CV)11.483728
Kurtosis892.0841
Mean0.1203266
Median Absolute Deviation (MAD)0
Skewness25.290755
Sum17080
Variance1.9093657
MonotonicityNot monotonic
2023-04-11T23:51:06.498602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 138008
97.2%
1 1721
 
1.2%
2 643
 
0.5%
3 361
 
0.3%
4 240
 
0.2%
5 192
 
0.1%
6 122
 
0.1%
7 94
 
0.1%
8 74
 
0.1%
9 63
 
< 0.1%
Other values (63) 429
 
0.3%
ValueCountFrequency (%)
0 138008
97.2%
1 1721
 
1.2%
2 643
 
0.5%
3 361
 
0.3%
4 240
 
0.2%
5 192
 
0.1%
6 122
 
0.1%
7 94
 
0.1%
8 74
 
0.1%
9 63
 
< 0.1%
ValueCountFrequency (%)
72 1
< 0.1%
71 1
< 0.1%
70 1
< 0.1%
69 1
< 0.1%
68 1
< 0.1%
67 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%

reserved_room_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
A
104124 
D
21453 
E
 
7530
F
 
3279
G
 
2371
Other values (5)
 
3190

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters141947
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowD
3rd rowE
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
A 104124
73.4%
D 21453
 
15.1%
E 7530
 
5.3%
F 3279
 
2.3%
G 2371
 
1.7%
B 1367
 
1.0%
C 1107
 
0.8%
H 691
 
0.5%
P 13
 
< 0.1%
L 12
 
< 0.1%

Length

2023-04-11T23:51:06.940292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T23:51:07.449275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
a 104124
73.4%
d 21453
 
15.1%
e 7530
 
5.3%
f 3279
 
2.3%
g 2371
 
1.7%
b 1367
 
1.0%
c 1107
 
0.8%
h 691
 
0.5%
p 13
 
< 0.1%
l 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 104124
73.4%
D 21453
 
15.1%
E 7530
 
5.3%
F 3279
 
2.3%
G 2371
 
1.7%
B 1367
 
1.0%
C 1107
 
0.8%
H 691
 
0.5%
P 13
 
< 0.1%
L 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 141947
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 104124
73.4%
D 21453
 
15.1%
E 7530
 
5.3%
F 3279
 
2.3%
G 2371
 
1.7%
B 1367
 
1.0%
C 1107
 
0.8%
H 691
 
0.5%
P 13
 
< 0.1%
L 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 141947
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 104124
73.4%
D 21453
 
15.1%
E 7530
 
5.3%
F 3279
 
2.3%
G 2371
 
1.7%
B 1367
 
1.0%
C 1107
 
0.8%
H 691
 
0.5%
P 13
 
< 0.1%
L 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 104124
73.4%
D 21453
 
15.1%
E 7530
 
5.3%
F 3279
 
2.3%
G 2371
 
1.7%
B 1367
 
1.0%
C 1107
 
0.8%
H 691
 
0.5%
P 13
 
< 0.1%
L 12
 
< 0.1%

assigned_room_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
A
88864 
D
29427 
E
9180 
F
 
4397
G
 
2955
Other values (7)
 
7124

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters141947
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowD
3rd rowE
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
A 88864
62.6%
D 29427
 
20.7%
E 9180
 
6.5%
F 4397
 
3.1%
G 2955
 
2.1%
C 2861
 
2.0%
B 2684
 
1.9%
H 847
 
0.6%
I 409
 
0.3%
K 308
 
0.2%
Other values (2) 15
 
< 0.1%

Length

2023-04-11T23:51:07.921449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 88864
62.6%
d 29427
 
20.7%
e 9180
 
6.5%
f 4397
 
3.1%
g 2955
 
2.1%
c 2861
 
2.0%
b 2684
 
1.9%
h 847
 
0.6%
i 409
 
0.3%
k 308
 
0.2%
Other values (2) 15
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 88864
62.6%
D 29427
 
20.7%
E 9180
 
6.5%
F 4397
 
3.1%
G 2955
 
2.1%
C 2861
 
2.0%
B 2684
 
1.9%
H 847
 
0.6%
I 409
 
0.3%
K 308
 
0.2%
Other values (2) 15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 141947
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 88864
62.6%
D 29427
 
20.7%
E 9180
 
6.5%
F 4397
 
3.1%
G 2955
 
2.1%
C 2861
 
2.0%
B 2684
 
1.9%
H 847
 
0.6%
I 409
 
0.3%
K 308
 
0.2%
Other values (2) 15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 141947
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 88864
62.6%
D 29427
 
20.7%
E 9180
 
6.5%
F 4397
 
3.1%
G 2955
 
2.1%
C 2861
 
2.0%
B 2684
 
1.9%
H 847
 
0.6%
I 409
 
0.3%
K 308
 
0.2%
Other values (2) 15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 88864
62.6%
D 29427
 
20.7%
E 9180
 
6.5%
F 4397
 
3.1%
G 2955
 
2.1%
C 2861
 
2.0%
B 2684
 
1.9%
H 847
 
0.6%
I 409
 
0.3%
K 308
 
0.2%
Other values (2) 15
 
< 0.1%

booking_changes
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21393901
Minimum0
Maximum21
Zeros120861
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-11T23:51:08.269389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.63647896
Coefficient of variation (CV)2.9750487
Kurtosis85.953003
Mean0.21393901
Median Absolute Deviation (MAD)0
Skewness6.1525617
Sum30368
Variance0.40510546
MonotonicityNot monotonic
2023-04-11T23:51:08.639504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 120861
85.1%
1 15064
 
10.6%
2 4271
 
3.0%
3 1044
 
0.7%
4 415
 
0.3%
5 136
 
0.1%
6 64
 
< 0.1%
7 33
 
< 0.1%
8 18
 
< 0.1%
9 8
 
< 0.1%
Other values (11) 33
 
< 0.1%
ValueCountFrequency (%)
0 120861
85.1%
1 15064
 
10.6%
2 4271
 
3.0%
3 1044
 
0.7%
4 415
 
0.3%
5 136
 
0.1%
6 64
 
< 0.1%
7 33
 
< 0.1%
8 18
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 2
 
< 0.1%
18 1
 
< 0.1%
17 3
< 0.1%
16 2
 
< 0.1%
15 3
< 0.1%
14 5
< 0.1%
13 5
< 0.1%
12 2
 
< 0.1%
11 3
< 0.1%

deposit_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
No Deposit
123396 
Non Refund
18385 
Refundable
 
166

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1419470
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit 123396
86.9%
Non Refund 18385
 
13.0%
Refundable 166
 
0.1%

Length

2023-04-11T23:51:09.005280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T23:51:09.441053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
no 123396
43.5%
deposit 123396
43.5%
non 18385
 
6.5%
refund 18385
 
6.5%
refundable 166
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 265177
18.7%
e 142113
10.0%
N 141781
10.0%
141781
10.0%
s 123396
8.7%
i 123396
8.7%
t 123396
8.7%
p 123396
8.7%
D 123396
8.7%
n 36936
 
2.6%
Other values (7) 74702
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 993961
70.0%
Uppercase Letter 283728
 
20.0%
Space Separator 141781
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 265177
26.7%
e 142113
14.3%
s 123396
12.4%
i 123396
12.4%
t 123396
12.4%
p 123396
12.4%
n 36936
 
3.7%
f 18551
 
1.9%
u 18551
 
1.9%
d 18551
 
1.9%
Other values (3) 498
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 141781
50.0%
D 123396
43.5%
R 18551
 
6.5%
Space Separator
ValueCountFrequency (%)
141781
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1277689
90.0%
Common 141781
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 265177
20.8%
e 142113
11.1%
N 141781
11.1%
s 123396
9.7%
i 123396
9.7%
t 123396
9.7%
p 123396
9.7%
D 123396
9.7%
n 36936
 
2.9%
R 18551
 
1.5%
Other values (6) 56151
 
4.4%
Common
ValueCountFrequency (%)
141781
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1419470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 265177
18.7%
e 142113
10.0%
N 141781
10.0%
141781
10.0%
s 123396
8.7%
i 123396
8.7%
t 123396
8.7%
p 123396
8.7%
D 123396
8.7%
n 36936
 
2.6%
Other values (7) 74702
 
5.3%

agent
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct333
Distinct (%)0.3%
Missing19555
Missing (%)13.8%
Infinite0
Infinite (%)0.0%
Mean85.685854
Minimum1
Maximum535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-11T23:51:09.877499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median14
Q3229
95-th percentile250
Maximum535
Range534
Interquartile range (IQR)220

Descriptive statistics

Standard deviation109.6286
Coefficient of variation (CV)1.2794247
Kurtosis-0.094985221
Mean85.685854
Median Absolute Deviation (MAD)13
Skewness1.071188
Sum10487263
Variance12018.429
MonotonicityNot monotonic
2023-04-11T23:51:10.376300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 34292
24.2%
240 16686
11.8%
1 11395
 
8.0%
6 4506
 
3.2%
14 4021
 
2.8%
7 3878
 
2.7%
250 3357
 
2.4%
241 2052
 
1.4%
28 1977
 
1.4%
3 1781
 
1.3%
Other values (323) 38447
27.1%
(Missing) 19555
13.8%
ValueCountFrequency (%)
1 11395
 
8.0%
2 208
 
0.1%
3 1781
 
1.3%
4 86
 
0.1%
5 492
 
0.3%
6 4506
 
3.2%
7 3878
 
2.7%
8 1765
 
1.2%
9 34292
24.2%
10 320
 
0.2%
ValueCountFrequency (%)
535 3
 
< 0.1%
531 68
< 0.1%
527 35
< 0.1%
526 10
 
< 0.1%
510 2
 
< 0.1%
509 10
 
< 0.1%
508 6
 
< 0.1%
502 24
 
< 0.1%
497 1
 
< 0.1%
495 57
< 0.1%

company
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct352
Distinct (%)4.3%
Missing133822
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean183.23606
Minimum6
Maximum543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-11T23:51:11.548715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile40
Q151
median174
Q3269
95-th percentile418.8
Maximum543
Range537
Interquartile range (IQR)218

Descriptive statistics

Standard deviation128.92195
Coefficient of variation (CV)0.70358394
Kurtosis-0.46230987
Mean183.23606
Median Absolute Deviation (MAD)107
Skewness0.60858559
Sum1488793
Variance16620.869
MonotonicityNot monotonic
2023-04-11T23:51:12.028136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 1165
 
0.8%
223 981
 
0.7%
45 312
 
0.2%
281 269
 
0.2%
67 268
 
0.2%
153 215
 
0.2%
174 170
 
0.1%
154 151
 
0.1%
219 141
 
0.1%
405 119
 
0.1%
Other values (342) 4334
 
3.1%
(Missing) 133822
94.3%
ValueCountFrequency (%)
6 1
 
< 0.1%
8 2
 
< 0.1%
9 55
< 0.1%
10 1
 
< 0.1%
11 2
 
< 0.1%
12 17
 
< 0.1%
14 9
 
< 0.1%
16 7
 
< 0.1%
18 1
 
< 0.1%
20 69
< 0.1%
ValueCountFrequency (%)
543 2
 
< 0.1%
541 1
 
< 0.1%
539 2
 
< 0.1%
534 2
 
< 0.1%
531 1
 
< 0.1%
530 5
 
< 0.1%
528 2
 
< 0.1%
525 15
< 0.1%
523 19
< 0.1%
521 7
 
< 0.1%

days_in_waiting_list
Real number (ℝ)

Distinct128
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3055648
Minimum0
Maximum391
Zeros137500
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-11T23:51:12.536560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.938253
Coefficient of variation (CV)7.3466829
Kurtosis185.29882
Mean2.3055648
Median Absolute Deviation (MAD)0
Skewness11.706837
Sum327268
Variance286.90442
MonotonicityNot monotonic
2023-04-11T23:51:13.052111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 137500
96.9%
58 328
 
0.2%
39 227
 
0.2%
87 160
 
0.1%
44 141
 
0.1%
31 127
 
0.1%
69 125
 
0.1%
50 110
 
0.1%
122 109
 
0.1%
77 101
 
0.1%
Other values (118) 3019
 
2.1%
ValueCountFrequency (%)
0 137500
96.9%
1 12
 
< 0.1%
2 5
 
< 0.1%
3 59
 
< 0.1%
4 25
 
< 0.1%
5 8
 
< 0.1%
6 26
 
< 0.1%
7 4
 
< 0.1%
8 7
 
< 0.1%
9 16
 
< 0.1%
ValueCountFrequency (%)
391 45
< 0.1%
379 15
 
< 0.1%
330 15
 
< 0.1%
259 10
 
< 0.1%
236 35
< 0.1%
224 10
 
< 0.1%
223 61
< 0.1%
215 21
 
< 0.1%
207 15
 
< 0.1%
193 1
 
< 0.1%

customer_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Transient
101474 
Transient-Party
32954 
Contract
 
6747
Group
 
772

Length

Max length15
Median length9
Mean length10.323656
Min length5

Characters and Unicode

Total characters1465412
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient 101474
71.5%
Transient-Party 32954
 
23.2%
Contract 6747
 
4.8%
Group 772
 
0.5%

Length

2023-04-11T23:51:13.530038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T23:51:13.968184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
transient 101474
71.5%
transient-party 32954
 
23.2%
contract 6747
 
4.8%
group 772
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 275603
18.8%
t 180876
12.3%
r 174901
11.9%
a 174129
11.9%
T 134428
9.2%
s 134428
9.2%
i 134428
9.2%
e 134428
9.2%
y 32954
 
2.2%
- 32954
 
2.2%
Other values (7) 56283
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1257557
85.8%
Uppercase Letter 174901
 
11.9%
Dash Punctuation 32954
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 275603
21.9%
t 180876
14.4%
r 174901
13.9%
a 174129
13.8%
s 134428
10.7%
i 134428
10.7%
e 134428
10.7%
y 32954
 
2.6%
o 7519
 
0.6%
c 6747
 
0.5%
Other values (2) 1544
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
T 134428
76.9%
P 32954
 
18.8%
C 6747
 
3.9%
G 772
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 32954
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1432458
97.8%
Common 32954
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 275603
19.2%
t 180876
12.6%
r 174901
12.2%
a 174129
12.2%
T 134428
9.4%
s 134428
9.4%
i 134428
9.4%
e 134428
9.4%
y 32954
 
2.3%
P 32954
 
2.3%
Other values (6) 23329
 
1.6%
Common
ValueCountFrequency (%)
- 32954
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1465412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 275603
18.8%
t 180876
12.3%
r 174901
11.9%
a 174129
11.9%
T 134428
9.2%
s 134428
9.2%
i 134428
9.2%
e 134428
9.2%
y 32954
 
2.2%
- 32954
 
2.2%
Other values (7) 56283
 
3.8%

adr
Real number (ℝ)

Distinct8879
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.416916
Minimum-6.38
Maximum5400
Zeros2555
Zeros (%)1.8%
Negative1
Negative (%)< 0.1%
Memory size1.1 MiB
2023-04-11T23:51:14.410681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-6.38
5-th percentile37.4
Q165.75
median90.95
Q3123
95-th percentile190
Maximum5400
Range5406.38
Interquartile range (IQR)57.25

Descriptive statistics

Standard deviation49.675549
Coefficient of variation (CV)0.49966898
Kurtosis914.64899
Mean99.416916
Median Absolute Deviation (MAD)27.55
Skewness9.4554177
Sum14111933
Variance2467.6602
MonotonicityNot monotonic
2023-04-11T23:51:14.892114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 6172
 
4.3%
75 3252
 
2.3%
90 3051
 
2.1%
65 2997
 
2.1%
0 2555
 
1.8%
80 2181
 
1.5%
60 1859
 
1.3%
100 1856
 
1.3%
95 1777
 
1.3%
120 1731
 
1.2%
Other values (8869) 114516
80.7%
ValueCountFrequency (%)
-6.38 1
 
< 0.1%
0 2555
1.8%
0.26 1
 
< 0.1%
0.5 1
 
< 0.1%
1 15
 
< 0.1%
1.29 1
 
< 0.1%
1.48 1
 
< 0.1%
1.56 2
 
< 0.1%
1.6 2
 
< 0.1%
1.8 1
 
< 0.1%
ValueCountFrequency (%)
5400 1
< 0.1%
510 1
< 0.1%
508 2
< 0.1%
451.5 1
< 0.1%
450 1
< 0.1%
437 1
< 0.1%
426.25 1
< 0.1%
402 1
< 0.1%
397.38 1
< 0.1%
392 2
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
133180 
1
 
8730
2
 
32
3
 
3
8
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters141947
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 133180
93.8%
1 8730
 
6.2%
2 32
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Length

2023-04-11T23:51:15.335835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T23:51:15.772279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 133180
93.8%
1 8730
 
6.2%
2 32
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 133180
93.8%
1 8730
 
6.2%
2 32
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 141947
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 133180
93.8%
1 8730
 
6.2%
2 32
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 141947
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 133180
93.8%
1 8730
 
6.2%
2 32
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 133180
93.8%
1 8730
 
6.2%
2 32
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

total_of_special_requests
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54967699
Minimum0
Maximum5
Zeros86024
Zeros (%)60.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-04-11T23:51:16.109155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7865411
Coefficient of variation (CV)1.4309151
Kurtosis1.577907
Mean0.54967699
Median Absolute Deviation (MAD)0
Skewness1.3946317
Sum78025
Variance0.6186469
MonotonicityNot monotonic
2023-04-11T23:51:16.435835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 86024
60.6%
1 37564
26.5%
2 15066
 
10.6%
3 2887
 
2.0%
4 362
 
0.3%
5 44
 
< 0.1%
ValueCountFrequency (%)
0 86024
60.6%
1 37564
26.5%
2 15066
 
10.6%
3 2887
 
2.0%
4 362
 
0.3%
5 44
 
< 0.1%
ValueCountFrequency (%)
5 44
 
< 0.1%
4 362
 
0.3%
3 2887
 
2.0%
2 15066
 
10.6%
1 37564
26.5%
0 86024
60.6%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Check-Out
89198 
Canceled
51279 
No-Show
 
1470

Length

Max length9
Median length9
Mean length8.6180335
Min length7

Characters and Unicode

Total characters1223304
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCanceled
2nd rowCanceled
3rd rowCanceled
4th rowCanceled
5th rowCanceled

Common Values

ValueCountFrequency (%)
Check-Out 89198
62.8%
Canceled 51279
36.1%
No-Show 1470
 
1.0%

Length

2023-04-11T23:51:16.813613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T23:51:17.277544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
check-out 89198
62.8%
canceled 51279
36.1%
no-show 1470
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 191756
15.7%
C 140477
11.5%
c 140477
11.5%
h 90668
7.4%
- 90668
7.4%
u 89198
7.3%
t 89198
7.3%
O 89198
7.3%
k 89198
7.3%
a 51279
 
4.2%
Other values (7) 161187
13.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 900021
73.6%
Uppercase Letter 232615
 
19.0%
Dash Punctuation 90668
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 191756
21.3%
c 140477
15.6%
h 90668
10.1%
u 89198
9.9%
t 89198
9.9%
k 89198
9.9%
a 51279
 
5.7%
n 51279
 
5.7%
l 51279
 
5.7%
d 51279
 
5.7%
Other values (2) 4410
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
C 140477
60.4%
O 89198
38.3%
N 1470
 
0.6%
S 1470
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 90668
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1132636
92.6%
Common 90668
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 191756
16.9%
C 140477
12.4%
c 140477
12.4%
h 90668
8.0%
u 89198
7.9%
t 89198
7.9%
O 89198
7.9%
k 89198
7.9%
a 51279
 
4.5%
n 51279
 
4.5%
Other values (6) 109908
9.7%
Common
ValueCountFrequency (%)
- 90668
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1223304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 191756
15.7%
C 140477
11.5%
c 140477
11.5%
h 90668
7.4%
- 90668
7.4%
u 89198
7.3%
t 89198
7.3%
O 89198
7.3%
k 89198
7.3%
a 51279
 
4.2%
Other values (7) 161187
13.2%
Distinct925
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2018-10-21
 
1461
2019-01-01
 
951
2019-07-06
 
931
2019-11-25
 
842
2018-07-06
 
805
Other values (920)
136957 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1419470
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)< 0.1%

Sample

1st row2018-05-06
2nd row2018-04-22
3rd row2018-06-23
4th row2018-05-11
5th row2018-05-29

Common Values

ValueCountFrequency (%)
2018-10-21 1461
 
1.0%
2019-01-01 951
 
0.7%
2019-07-06 931
 
0.7%
2019-11-25 842
 
0.6%
2018-07-06 805
 
0.6%
2018-01-01 763
 
0.5%
2019-01-18 633
 
0.4%
2019-07-02 597
 
0.4%
2019-12-07 534
 
0.4%
2018-07-02 469
 
0.3%
Other values (915) 133961
94.4%

Length

2023-04-11T23:51:17.624785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-10-21 1461
 
1.0%
2019-01-01 951
 
0.7%
2019-07-06 931
 
0.7%
2019-11-25 842
 
0.6%
2018-07-06 805
 
0.6%
2018-01-01 763
 
0.5%
2019-01-18 633
 
0.4%
2019-07-02 597
 
0.4%
2019-12-07 534
 
0.4%
2018-07-02 469
 
0.3%
Other values (915) 133961
94.4%

Most occurring characters

ValueCountFrequency (%)
0 356903
25.1%
- 283894
20.0%
2 257810
18.2%
1 228293
16.1%
9 107594
 
7.6%
8 53318
 
3.8%
3 30540
 
2.2%
7 29293
 
2.1%
6 24754
 
1.7%
5 23853
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1135576
80.0%
Dash Punctuation 283894
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 356903
31.4%
2 257810
22.7%
1 228293
20.1%
9 107594
 
9.5%
8 53318
 
4.7%
3 30540
 
2.7%
7 29293
 
2.6%
6 24754
 
2.2%
5 23853
 
2.1%
4 23218
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 283894
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1419470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 356903
25.1%
- 283894
20.0%
2 257810
18.2%
1 228293
16.1%
9 107594
 
7.6%
8 53318
 
3.8%
3 30540
 
2.2%
7 29293
 
2.1%
6 24754
 
1.7%
5 23853
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1419470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 356903
25.1%
- 283894
20.0%
2 257810
18.2%
1 228293
16.1%
9 107594
 
7.6%
8 53318
 
3.8%
3 30540
 
2.2%
7 29293
 
2.1%
6 24754
 
1.7%
5 23853
 
1.7%

Interactions

2023-04-11T23:50:35.919091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:07.194218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:14.218016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:21.286433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:28.020333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:35.194782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:42.389337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:48.942244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:55.540234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:02.311736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:09.418224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:15.917607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:21.959093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:29.452366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:36.438286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:07.792636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:15.208635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:21.783791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:28.633204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:35.677507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:42.877403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:49.460483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:56.048980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:02.808677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:09.878887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:16.338688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:22.532540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:29.928355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:36.897463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:08.301482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:15.660995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:22.253116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:29.150829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:36.124238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:43.356464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:49.918526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:56.517333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:03.256808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:10.344706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:16.755065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:23.003956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:30.386287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:37.403376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:08.804548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:16.149800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:22.770631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:29.671421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:36.586448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:43.835747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:50.396681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:57.014821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:04.402688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:10.814023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:17.141495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:23.517530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:30.872141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:37.916971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:09.335968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:16.651062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:23.305916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:30.189437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:37.118077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:44.350435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:50.895200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:57.524583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:04.894206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:11.343650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:17.580884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:24.052697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:31.378189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:38.387664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:09.851254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:17.099825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:23.811427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:30.676404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:37.558901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:44.802723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:51.400204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:58.010016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:05.359662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:11.804464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:17.993036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:24.516929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:31.838183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:38.850704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:10.364925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:17.564772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:24.232439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:31.168263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:38.022260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:45.249835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:51.851647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:58.490235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:05.816109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:12.249257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:18.437247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:24.996798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:32.289036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:39.336175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:10.842177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:18.049494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:24.685454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:31.680220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:38.483249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:45.690849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:52.322614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:58.964669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:06.259930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:12.686895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:18.835806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:25.473237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:32.748101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:39.831419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:11.368937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:18.535932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:25.189464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:32.227977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:39.617635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:46.182767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:52.800986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:59.471700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:06.752792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:13.148431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:19.283452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:25.974629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:33.237736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:40.288528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:11.818356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:18.974421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:25.696162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:32.688174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:40.038910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:46.619720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:53.233387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:59.918081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:07.171457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:13.629910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:19.656605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:26.421953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:33.644695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:40.691216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:12.213218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:19.381984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:26.066015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:33.118062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:40.452585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:47.010145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:53.633736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:00.333115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:07.610601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:14.026411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:19.992831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:26.840137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:34.047662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:41.197896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:12.711271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:19.825726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:26.547554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:33.617076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:40.958408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:47.518643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:54.102010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:00.805699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:08.055066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:14.555039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:20.435637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:27.306710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:34.517109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:41.705553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:13.243951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:20.371740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:27.039714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:34.174383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:41.480824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:48.019516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:54.635211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:01.342178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:08.535181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:15.050763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:20.917831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:27.823534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:35.019760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:42.151148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:13.718733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:20.818418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:27.527797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:34.676881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:41.922965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:48.482939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:49:55.090101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:01.835483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:08.969360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:15.507396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:21.402444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:28.296463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T23:50:35.461388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-11T23:51:18.115861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
lead_timearrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultsprevious_cancellationsprevious_bookings_not_canceledbooking_changesagentcompanydays_in_waiting_listadrtotal_of_special_requestshotelis_canceledarrival_date_yeararrival_date_monthchildrenbabiesmealmarket_segmentdistribution_channelis_repeated_guestreserved_room_typeassigned_room_typedeposit_typecustomer_typerequired_car_parking_spacesreservation_status
lead_time1.0000.0430.0010.1560.2770.1940.235-0.180-0.020-0.1520.3080.137-0.005-0.0910.1050.3080.0990.1290.0300.0100.0880.1730.1160.1230.0500.0660.2860.1310.0580.227
arrival_date_week_number0.0431.0000.0650.0060.001-0.0140.116-0.0390.005-0.058-0.0670.017-0.031-0.0130.0800.0700.4020.8000.0590.0160.0930.0880.0690.0690.0440.0310.1020.1250.0190.064
arrival_date_day_of_month0.0010.0651.000-0.016-0.0140.006-0.0200.0000.0120.0030.0510.0420.0340.0070.0330.0220.0370.0650.0130.0060.0510.0400.0320.0210.0120.0110.0630.0400.0100.025
stays_in_weekend_nights0.1560.006-0.0161.0000.2380.130-0.055-0.0800.0380.1330.079-0.0760.0530.0810.2010.0220.0230.0460.0310.0110.0580.0580.0550.0820.0540.0510.0740.0750.0120.022
stays_in_week_nights0.2770.001-0.0140.2381.0000.147-0.070-0.1150.0670.1930.2590.0190.1060.0910.1980.0310.0110.0360.0160.0000.0410.0350.0070.0190.0460.0470.0490.0710.0150.031
adults0.194-0.0140.0060.1300.1471.000-0.020-0.202-0.090-0.0460.218-0.0310.2650.1610.0180.0170.0080.0130.0000.0000.0000.0130.0120.0000.0090.0070.0000.1100.0000.012
previous_cancellations0.2350.116-0.020-0.055-0.070-0.0201.0000.068-0.090-0.227-0.1550.061-0.170-0.1570.0620.0560.0340.0360.0030.0000.1010.0510.0500.1690.0090.0110.0640.0110.0000.040
previous_bookings_not_canceled-0.180-0.0390.000-0.080-0.115-0.2020.0681.0000.0310.058-0.270-0.019-0.1310.0240.0160.0390.0240.0180.0000.0000.0130.0890.0990.3000.0010.0030.0130.0150.0170.028
booking_changes-0.0200.0050.0120.0380.067-0.090-0.0900.0311.0000.0950.194-0.0120.0090.0450.0400.0490.0150.0110.0180.0140.0110.0210.0280.0000.0160.0570.0290.0290.0180.036
agent-0.152-0.0580.0030.1330.193-0.046-0.2270.0580.0951.0000.286-0.004-0.0090.0690.8230.0950.0930.0860.0590.0270.1820.2260.2040.0830.1450.1350.1250.1360.1330.070
company0.308-0.0670.0510.0790.2590.218-0.155-0.2700.1940.2861.000-0.0060.011-0.1090.5370.1100.2720.2370.0430.0560.2060.3870.2020.3370.0960.0860.1790.2480.0330.086
days_in_waiting_list0.1370.0170.042-0.0760.019-0.0310.061-0.019-0.012-0.004-0.0061.000-0.028-0.1210.0910.0490.0650.0650.0180.0000.0890.0750.0280.0250.0270.0270.1050.0810.0310.038
adr-0.005-0.0310.0340.0530.1060.265-0.170-0.1310.009-0.0090.011-0.0281.0000.2050.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.000
total_of_special_requests-0.091-0.0130.0070.0810.0910.161-0.1570.0240.0450.069-0.109-0.1210.2051.0000.0660.2580.0870.0530.0650.0640.0660.2210.0690.0370.0820.0720.2200.1180.0470.184
hotel0.1050.0800.0330.2010.1980.0180.0620.0160.0400.8230.5370.0910.0000.0661.0000.1370.0360.0850.0510.0490.3050.1550.1920.0320.3260.3930.1780.0670.2230.139
is_canceled0.3080.0700.0220.0220.0310.0170.0560.0390.0490.0950.1100.0490.0000.2580.1371.0000.0190.0730.0290.0350.0570.2800.1740.0630.0870.2270.4970.1120.1980.999
arrival_date_year0.0990.4020.0370.0230.0110.0080.0340.0240.0150.0930.2720.0650.0000.0870.0360.0191.0000.4030.0420.0080.1060.1420.0250.0100.0770.0470.0460.1670.0150.019
arrival_date_month0.1290.8000.0650.0460.0360.0130.0360.0180.0110.0860.2370.0650.0040.0530.0850.0730.4031.0000.0660.0190.1010.0940.0750.0690.0480.0290.1040.1240.0200.066
children0.0300.0590.0130.0310.0160.0000.0030.0000.0180.0590.0430.0180.0000.0650.0510.0290.0420.0661.0000.0250.0320.1020.0450.0320.3520.2970.0730.0660.0320.038
babies0.0100.0160.0060.0110.0000.0000.0000.0000.0140.0270.0560.0000.0000.0640.0490.0350.0080.0190.0251.0000.0140.0360.0290.0090.0420.0460.0240.0170.0210.024
meal0.0880.0930.0510.0580.0410.0000.1010.0130.0110.1820.2060.0890.0000.0660.3050.0570.1060.1010.0320.0141.0000.1980.0740.0610.0930.1060.0940.1410.0240.047
market_segment0.1730.0880.0400.0580.0350.0130.0510.0890.0210.2260.3870.0750.0000.2210.1550.2800.1420.0940.1020.0360.1981.0000.6930.3160.1420.1250.3640.2850.0930.206
distribution_channel0.1160.0690.0320.0550.0070.0120.0500.0990.0280.2040.2020.0280.0000.0690.1920.1740.0250.0750.0450.0290.0740.6931.0000.2680.1060.0990.0900.0890.0740.129
is_repeated_guest0.1230.0690.0210.0820.0190.0000.1690.3000.0000.0830.3370.0250.0000.0370.0320.0630.0100.0690.0320.0090.0610.3160.2681.0000.0370.0690.0550.0870.0640.064
reserved_room_type0.0500.0440.0120.0540.0460.0090.0090.0010.0160.1450.0960.0270.0000.0820.3260.0870.0770.0480.3520.0420.0930.1420.1060.0371.0000.7660.1530.1220.0820.062
assigned_room_type0.0660.0310.0110.0510.0470.0070.0110.0030.0570.1350.0860.0270.0000.0720.3930.2270.0470.0290.2970.0460.1060.1250.0990.0690.7661.0000.1980.0970.0960.162
deposit_type0.2860.1020.0630.0740.0490.0000.0640.0130.0290.1250.1790.1050.0060.2200.1780.4970.0460.1040.0730.0240.0940.3640.0900.0550.1530.1981.0000.0860.0720.357
customer_type0.1310.1250.0400.0750.0710.1100.0110.0150.0290.1360.2480.0810.0000.1180.0670.1120.1670.1240.0660.0170.1410.2850.0890.0870.1220.0970.0861.0000.0490.079
required_car_parking_spaces0.0580.0190.0100.0120.0150.0000.0000.0170.0180.1330.0330.0310.0000.0470.2230.1980.0150.0200.0320.0210.0240.0930.0740.0640.0820.0960.0720.0491.0000.139
reservation_status0.2270.0640.0250.0220.0310.0120.0400.0280.0360.0700.0860.0380.0000.1840.1390.9990.0190.0660.0380.0240.0470.2060.1290.0640.0620.1620.3570.0790.1391.000

Missing values

2023-04-11T23:50:43.587320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-11T23:50:46.589290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-11T23:50:49.724726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
0Resort Hotel1852018July2710320.00BBPRTOnline TATA/TO000AA0No Deposit240.0NaN0Transient82.0001Canceled2018-05-06
1Resort Hotel1752018July2710320.00HBPRTOffline TA/TOTA/TO000DD0No Deposit15.0NaN0Transient105.5000Canceled2018-04-22
2Resort Hotel1232018July2710420.00BBPRTOnline TATA/TO000EE0No Deposit240.0NaN0Transient123.0000Canceled2018-06-23
3Resort Hotel1602018July2712520.00BBPRTOnline TATA/TO000EE0No Deposit240.0NaN0Transient107.0002Canceled2018-05-11
4Resort Hotel1962018July2712820.00BBPRTDirectDirect000EE0No DepositNaNNaN0Transient108.3002Canceled2018-05-29
5Resort Hotel1452018July2721330.00BBPRTOnline TATA/TO000DD0No Deposit241.0NaN0Transient108.8001Canceled2018-05-19
6Resort Hotel1402018July2721330.00BBPRTOnline TATA/TO000DD0No Deposit241.0NaN0Transient108.8001Canceled2018-06-19
7Resort Hotel1432018July2721330.00BBPRTOnline TATA/TO000DD0No Deposit241.0NaN0Transient108.8000Canceled2018-05-23
8Resort Hotel1452018July2722320.00BBPRTOnline TATA/TO000GG0No Deposit241.0NaN0Transient117.8100Canceled2018-05-18
9Resort Hotel1472018July2722522.00BBPRTOnline TATA/TO000GG0No Deposit240.0NaN0Transient153.0000Canceled2018-06-02
hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
141937City Hotel12192020August35281321.00BBROUOnline TATA/TO000AD1No Deposit9.0NaN0Transient135.0002No-Show2020-08-28
141938City Hotel13202020August35282520.00BBDEUOnline TATA/TO000AA0No Deposit9.0NaN0Transient112.7603No-Show2020-08-29
141939City Hotel112020March1070220.00SCPRTDirectDirect101AA0No DepositNaNNaN0Transient98.0000No-Show2020-03-07
141940City Hotel142020August35281010.00BBPRTComplementaryCorporate103AA0No DepositNaN72.00Transient0.0002No-Show2020-08-28
141941City Hotel122020June24112110.00BBPRTAviationCorporate102AA0No DepositNaN153.00Transient95.0000No-Show2020-06-11
141942City Hotel112020February510010.00BBAUTAviationCorporate101AA0No DepositNaN153.00Transient0.0001No-Show2020-02-01
141943City Hotel1312020July29162010.00BBUSADirectDirect102AA1No DepositNaNNaN0Transient135.0002No-Show2020-07-16
141944City Hotel1252020May1862110.00BBFRACorporateCorporate101EF0No DepositNaN450.00Transient125.0000No-Show2020-05-06
141945City Hotel162020July29171010.00BBPRTCorporateCorporate111AD0No DepositNaN238.00Transient65.0000No-Show2020-07-17
141946City Hotel102020August3120210.00BBPRTCorporateCorporate100AC0No DepositNaN40.00Transient65.0001No-Show2020-08-02

Duplicate rows

Most frequently occurring

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date# duplicates
6589City Hotel12772019November4671220.00BBPRTGroupsTA/TO000AA0Non RefundNaNNaN0Transient100.000Canceled2019-04-04180
5192City Hotel1682019February8170220.00BBPRTGroupsTA/TO010AA0Non Refund37.0NaN0Transient75.000Canceled2019-01-06150
6208City Hotel11882019June25150210.00BBPRTOffline TA/TOTA/TO000AA0Non Refund119.0NaN39Transient130.000Canceled2019-01-18109
5995City Hotel11582019May22240210.00BBPRTGroupsTA/TO000AA0Non Refund37.0NaN31Transient130.000Canceled2019-01-18101
4809City Hotel1342018December5080210.00BBPRTOffline TA/TOTA/TO010AA0Non Refund19.0NaN0Transient90.000Canceled2018-11-17100
4814City Hotel1342019December5080210.00BBPRTOffline TA/TOTA/TO010AA0Non Refund19.0NaN0Transient90.000Canceled2019-11-17100
4739City Hotel1282020March920320.00BBPRTGroupsTA/TO000AA0Non RefundNaNNaN0Transient95.000Canceled2020-02-0299
4873City Hotel1382020January2140110.00BBPRTCorporateCorporate000AA0Non RefundNaN67.00Transient75.000Canceled2019-12-0799
5988City Hotel11562020April17260320.00BBPRTGroupsTA/TO000AA0Non Refund37.0NaN0Transient100.000Canceled2019-11-2199
5221City Hotel1712019June25140310.00BBPRTOffline TA/TOTA/TO000AA0Non Refund236.0NaN0Transient120.000Canceled2019-04-2789